Adverse drug reactions (ADRs) are among the top five causes of death in the United States and may contribute a financial burden of as much as $4 billion per year1. ADRs may occur when toxic concentrations of drug are reached in a patient or when suboptimal drug administration reduces therapeutic efficacy. Genetic polymorphisms in drug metabolizing enzymes are one of the many factors contributing to variability in therapeutic efficacy. In particular, genetic variability in drug metabolism is largely associated with the cytochrome P450 (CYP450) enzyme family, which is responsible for the metabolism of over 75% of commonly-prescribed drugs2.
Several variations in the genes encoding for CYP450 enzymes have been associated with increased or decreased metabolism of many drug classes. Depending on enzyme activity, patients with variants may require dose modification to avoid toxic effects or ensure efficacy. Sometimes, a particular drug should not be used at all in a patient with a certain genetic polymorphism due to potential deleterious effects. The FDA lists its recommendations for pharmacogenetic testing on >100 drug labels3. As an example, the label for clopidogrel (Plavix) warns that patients who are CYP2C19 poor metabolizers may experience diminished drug effectiveness and are therefore at higher risk of adverse cardiovascular events. The drug label recommends considering alternative treatments in these patients, who may occur at a frequency of 2-14% in the population, depending on ethnicity. There are increasing evidence-based resources available to assist clinicians in interpreting and utilizing genetic test results to optimize drug therapy. A chief resource is the Clinical Pharmacogenetics Implementation Consortium (CPIC), which is a set of guidelines, organized both by drug and by gene, designed to help clinicians understand how to use specific genetic test results to guide therapy.4
However, obtaining pharmacogenetic results in the first place can still be a challenge. Current laboratory methods used to genotype patients for CYP450 variants are limited in that they are often expensive, complex, and not performed on routine analyzers. Consequently, these tests are most often performed in reference labs, and there is an inability to obtain same-day results. This can either cause a delay in the patient’s drug selection/dosing or cause the patient to be dosed prematurely, increasing the chance of an adverse event if the patient does indeed possess a relevant mutation. Additionally, even when genotype testing is available and performed, results can be difficult to interpret without the use of computational decision support.
Fortunately, some institutions are paving the way to make routine pharmacogenetic testing a reality. At the 2015 AACC Annual Meeting, Dr. John Black discussed Mayo Clinic’s Personalized Genomics Lab, which uses combinations of Sanger sequencing, real-time PCR, and next-generation sequencing platforms to perform simple single-gene or small-panel pharmacogenetic testing for CYP and other variants. Results are incorporated into the electronic health record, and pre- and post-testing consultations with pharmacists are available to determine proper test utility and result interpretation. Additionally, St. Jude Children’s Research Hospital is performing pre-emptive genotype testing on patients in order to maintain information in the health record that may be useful down the line as patients’ drug regimens are changed. Information can be sorted both by drug and by gene.
In summary, the correlation of specific polymorphisms (genotype) to drug response (phenotype) is strongly implicated in a large number of clinical scenarios. Over the next several years, it is likely that more and more patients will have pharmacogenetically-relevant data in their medical records. It will be critical for the laboratories to offer medical decision support by incorporating recommended guidelines (i.e., CPIC) into the patient’s laboratory information. This will allow for increasingly-complex genetic data to be interpreted in an effective way to ultimately minimize ADRs.
- Pirmohamed, M. Personalized pharmacogenomics: Predicting efficacy and adverse drug reactions. Annu Rev Genomics Hum Genet. 2014; 15: 349-70.
- Zanger UM and Schwab M. Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol Ther. 2013 138(1): 103-41.
- US Food and Drug Administration (FDA) (2015) Table of Pharmacogenomic Biomarkers in Drug Labeling.
- Clinical Pharmacogenetics Implementation Consortium (CPIC) Dosing Guidelines. www.pharmgkb.org.